Multi-Semantic Markov Random Field Model for Semantic Segmentation of High-Resolution Remote Sensing Images-Reference-Cited by-同舟云学术

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Multi-Semantic Markov Random Field Model for Semantic Segmentation of High-Resolution Remote Sensing Images

Published:2024-07-07 Issue: Volume:24 Page:9819-9822
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Fu Haoyu¹,Zheng Chen²,Xu Weiheng³,Zhao Yili³,Wang Leiguang⁴

Affiliation:

1. Southwest Forestry University,Faculty of Forestry,Kunming,China

2. Henan University,School of Mathematics and Statistics,Kaifeng,China

3. Southwest Forestry University,College of Big Data and Intelligent Engineering,Kunming,China

4. Southwest Forestry University,College of Landscape Architecture and Horticulture,Kunming,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642613.pdf?arnumber=10642613

Reference11 articles.

1. Deep learning-based individual tree crown delineation in mangrove forests using very-high-resolution satellite imagery

2. Improving building rooftop segmentation accuracy through the optimization of UNet basic elements and image foreground-background balance

3. Very fine spatial resolution urban land cover mapping using an explicable sub-pixel mapping network based on learnable spatial correlation

4. Feature-guided dynamic graph convolutional network for wetland hyperspectral image classification

5. High-Resolution Remote Sensing Image Semantic Segmentation via Multiscale Context and Linear Self-Attention

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